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A self-adaptive piston is designed for the compressional gas cushion press nanoimprint lithography system. It avoids the lube pollution and high wear of traditional piston.

The self-adaptive piston device consists of symmetrical piston bodies, piston rings and other parts. The two piston bodies are linked by a ball-screw. The locking nut adjusts the distance between two piston bodies to avoid the piston rings from being stuck. The piston rings are placed between two piston bodies.

The simulation results based on COMSOL indicate that cylinder vibration caused by self-adaptive piston is 15.9 times smaller than the one caused by a traditional piston.

The self-adaptive piston is superior to the traditional piston in decreasing cylinder vibration.

The paper presents a wide range of post processing heat treatment cycles performed to Electron Beam Melted (EBM) Ti6Al4V alloy and establishes correlations of heat treat process to microstructure and mechanical property (microhardness). The research also identifies the optimal heat treatment to obtain the best microstructure and mechanical properties (hardness and tensile).

Rectangular bars fabricated using EBM was used to study the different heat treatment cycles. A variety of heat treatments from sub ß-transus, super ß-transus, near ß-transus and solution aircool plus ageing were designed. After the heat treatment process, the samples were analysed for, α lath width, prior ß grain size, microhardness and nanohardness. Tensile tests were done for the heat treated samples showing most refined α lath structure with uniform globular grains.

A clear correlation was observed between α lath width and the microhardness values. The solution aircooled plus aged samples exhibited the best refinement in α-ß morphology with uniform equiaxed grains. The tensile properties of the solution aircooled plus aged samples were comparable to that of the EBM printed samples and better than ASTMF1472 specifications.

There is hardly any prior work related to post processing heat treatment of EBM built Ti6Al4V other than HIP treatments. The variety of heat treatment cycles and its influence in microstructure and properties, studied in this research, gives a clear understanding on how to tailor final microstructures and select the optimal heat treatment process.

In recent decades, the responsibility for the financing of compulsory education in rural China has rested with townships and villages which, with limited tax authority and uneven revenue capacity, increasingly relied on a plethora of arbitrarily imposed fees for funding. To reduce farmers’ fiscal burdens, in 2000, the central government installed a series of rural taxation reforms. Correspondingly, the central government shifted the administrative responsibilities of rural compulsory education to the county level in 2001, and implemented a series of policies to make up for the loss of revenues to education. Using a provincial-level dataset from 1998 to 2006, we examined whether and how the rural taxation reforms affected the adequacy and equity of compulsory education finance in China, addressing related theoretical and policy implications from the perspective of intergovernmental fiscal relations.

The purpose of this paper is to preset a spherical element subdivision method for the numerical evaluation of nearly singular integrals in three-dimensional (3D) boundary element method (BEM).

In this method, the source point is first projected to the tangent plane of the element. Then two cases are considered: the projection point is either inside or outside the element. In both cases, the element is subdivided into a number of patches using a sequence of spheres with decreasing radius.

With the proposed method, the patches obtained are automatically refined as they approach the projection point and each patch of the integration element is “good” in shape and size for standard Gaussian quadrature. Therefore, all kinds of nearly singular boundary integrals on elements of any shape and size with arbitrary source point location related to the element can be evaluated accurately and efficiently.

Numerical examples for planar and slender elements with various relative location of the source point are presented. The results demonstrate that our method has much better accuracy, efficiency and stability than conventional methods.

Design is a time-consuming process for mechanical production. Some design structures frequently occur in different products and can be shared by multiple assembly models. Thus, identifying these structures and adding them to a design knowledge library significantly speed up the design process. Most studies addressing this issue have traditionally focused on part models and have not extended to assembly models. This paper aims to find a method for common design structure discovery in assembly models.

Computer-aided design models have a great deal of valuable information defined by different designers in the design stages, especially the assembly models, which are actually carriers of information from multiple sources. In this paper, an approach for discovering a common design structure in assembly models is proposed by comparing information from multiple sources. Assembly models are first represented as attribute connection graphs (ACGs), in which we mainly consider topological information and various attributes of parts and connections. Then, we apply a K-means clustering method based on a similarity analysis of different attributes to classify the parts and connections and transform ACGs of assemblies into type code graphs (TCGs). After this, a discovery algorithm that improves upon fast frequent subgraph mining is used to identify common design structures in assemblies.

A new method was developed for discovering common design structures in assembly models, considering the similarity of information from multiple sources and allowing some differences in the details to keep both commonalities and individualities of common design structures.

Experiments show that the proposed method is efficient and can produce a reasonable result.

This discovery method helps designers find common design structures from different assembly models and shorten the design cycle. It is an effective approach to build a knowledge library for product design that can shorten the design cycle.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.

Relying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.

The results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.

By applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.

Thermal conduction anisotropy, which is defined by the dependency of thermal conductivity on direction, is an important parameter in many engineering and research studies such as the design of nuclear waste depositional sites. In this context, the authors aim to investigate the effect of grain shape in thermal conduction anisotropy using pore scale modeling that utilizes real shapes of grains, pores and throats to characterize petrophysical properties of a porous medium.

The authors generalize the swelling circle approach to generate porous media composed of randomly arranged but regularly oriented elliptical grains at various grain ratios and porosities. Unlike previous studies that use fitting parameters to capture the effect of grain–grain thermal contact resistance, the authors apply roughness to grains’ surface. The authors utilize Lattice Boltzmann method to solve steady state heat conduction through medium.

Based on the results, when the temperature field is not parallel to either major or minor axes of grains, the overall heat flux vector makes a “deviation angle” with the temperature field. Deviation angle increases by augmenting the ratio of thermal conductivities of solid to fluid and the aspect ratios of grains. In addition, the authors show that porosity and surface roughness can considerably change the anisotropic properties of a porous medium whose grains are elliptical in shape.

The authors developed an algorithm for generation of non-circular-based porous medium with a novel approach to include grain surface roughness. In previous studies, the effect of grain contacts has been simulated using fitting parameters, whereas in this work, the authors impose the roughness based on the its fractal geometry.

The change rules of the shielding effectiveness (SE) of the sleeve has not been clarified, which leads to the lack of the basis for the design, manufacture and evaluation of the electromagnetic shielding (EMS) clothing.

According to a simplified analysis model, a series of sleeve samples with different fabrics and styles are designed and manufactured. The SE of the sleeve is tested with the proposed special test method in a semi-anechoic chamber to analyze the influence of different factors on the SE of the sleeve.

The SE is greatly reduced about 60–90% after the fabric is manufactured into the sleeve. The larger the sleeve length is, the higher the peak value of the SE is. When the sleeve length is low, the SE value is easy to appear negative. As the cuff circumference increases, the SE of the sleeve will change with the frequency band. The influence of the cuff style on the SE of the sleeve mainly depends on the cuff width and style. The larger the cuff width is, the lower the overall SE of the sleeve is. The more wrinkles there are at the cuff, the better the SE of the sleeve is.

Our results provide a reference for the design, production and evaluation of the sleeve and the whole EMS clothing.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.